import numpy as np
import pandas as pd
import geopandas as gpd
import fiona
import glob
import os
import contextily as ctx
from scipy.spatial import cKDTree
from shapely.geometry import Point, shape, LineString, MultiLineString, GeometryCollection, MultiPoint, Polygon, MultiPolygon  # creating points
import json
from tqdm.auto import tqdm
from tqdm.contrib.concurrent import process_map, thread_map
pd.set_option('min_rows', 30)
import sys
from importlib import reload
from util import *
import matplotlib.pyplot as plt
import multiprocessing
# from pandarallel import pandarallel # parallel operations for speed
# pandarallel.initialize(nb_workers=8, progress_bar=True)
# import swifter
tqdm.pandas()
plt.rcParams['figure.figsize'] = (16, 16)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 10)
gpd.options.use_pygeos 

True

ls('restricted')

	name	filesize (MB)	last modified
0	2016_Unitary_Plan_operational_15Nov	0.00	2021-09-02 04:46:20.562659
1	BCs_issued_by_AUP_TLADCs_2021FEB.csv	64.34	2021-09-23 04:36:31.125500

Total: 64.0MB

Setup

%%time
parcels = gpd.read_file('input/lds-nz-primary-parcels-CLIPPED-4326.gpkg').to_crs(4326)
parcels['geometry_polygon_4326'] = parcels.geometry.to_crs(4326)
parcels['representative_point_4326'] = parcels.representative_point()

/data/miniconda3/envs/house-upzone/lib/python3.8/site-packages/geopandas/geodataframe.py:577: RuntimeWarning: Sequential read of iterator was interrupted. Resetting iterator. This can negatively impact the performance.
  for feature in features_lst:

CPU times: user 1min 25s, sys: 3.54 s, total: 1min 28s
Wall time: 1min 28s

parcels_sample = parcels.sample(10000)

# keep a centroid and polygon version - change between them according to need
parcels_sample['geometry_polygon_2193'] = parcels_sample.geometry.to_crs(2193)
parcels_sample['geometry_centroid_2193'] = parcels_sample.geometry_polygon_2193.centroid
parcels_sample['geometry_centroid_4326'] = parcels_sample.geometry_centroid_2193.to_crs(4326)
parcels_sample['geometry_polygon_4326'] = parcels_sample.geometry_polygon_2193.to_crs(4326)

# bounds of the parcel dataset
bounds2193 = {'x1': 1.5e6, 'x2':2e6, 'y1':5.8e6, 'y2':6.1e6}

# get a sample of non-road polygons
parcels_sample2 = parcels_sample[parcels_sample.parcel_intent != 'Road'].sample(5)

parcels.sindex
parcels_sample.sindex
parcels_sample2.sindex

<geopandas.sindex.PyGEOSSTRTreeIndex at 0x7f8c6f864f10>

1. LINZ parcel information

a. LINZ_parcel_ID Parcel identifier - “certificate of title”.

parcels_sample['LINZ_parcel_ID'] = parcels_sample.id

b. LINZ_parcel_centroid_lon

c. LINZ_parcel_centroid_lat

parcels_sample['geometry'] = parcels_sample.geometry_centroid_4326
parcels_sample['LINZ_parcel_centroid_lon'] = parcels_sample.geometry.x
parcels_sample['LINZ_parcel_centroid_lat'] = parcels_sample.geometry.y

d. LINZ_parcel_vertices_lon vector of longitudes of the vertices of the matched LINZ parcels

e. LINZ_parcel_vertices_lat

parcels_sample['geometry'] = parcels_sample.geometry_polygon_4326
parcels_sample = parcels_sample.set_crs(4326)
# all are multipolygons
print(set([type(a) for a in parcels_sample.geometry]))

{<class 'shapely.geometry.multipolygon.MultiPolygon'>}

%%time
def extract_verts(geometry):
    lat = np.nan
    lng = np.nan
    if geometry:
        coordinates = []
        for polygon in geometry:
            # the last point is the same as the first
            coordinates.extend(polygon.exterior.coords[:-1])
        lng = f"[{'; '.join([str(round(point[0], 6)) for point in coordinates])}]"
        lat = f"[{'; '.join([str(round(point[1], 6)) for point in coordinates])}]"
    return pd.Series([lng, lat])
parcels_sample[["LINZ_parcel_vertices_lon", "LINZ_parcel_vertices_lat"]] = parcels_sample.geometry.apply(extract_verts)

CPU times: user 6.93 s, sys: 49.1 ms, total: 6.98 s
Wall time: 6.96 s

f. LINZ_parcel_roadvertices_lon subvector of longitudes of parcel that sits adjacent to a road

g. LINZ_parcel_roadvertices_lat

roads = parcels[parcels.parcel_intent == "Road"]
roads = roads.to_crs(4326)

%%time
roads_dissolved = roads.dissolve()

CPU times: user 30.6 s, sys: 146 ms, total: 30.7 s
Wall time: 30.7 s

parcels_sample2['geometry'] = parcels_sample2.geometry_polygon_4326
parcels_sample2 = parcels_sample2.set_crs(4326)

def get_points_in_roads(geom):
    """return a list of points from the geometry that fall within roads_dissolved"""
    # assume multipolygon
    assert isinstance(geom, MultiPolygon), f"not implemented for geometry of type {type(geom)}"
    road_points = []
    # iterate over polygons
    for poly in geom:
        # split polygon into vertices
        # the last point is the same as the first
        coords = poly.exterior.coords[:-1]
        pointsx = [x for x, _ in coords]
        pointsy = [y for _, y in coords]
        # create gdf with one row per vertex
        points_gdf = gpd.GeoDataFrame(geometry=gpd.points_from_xy(pointsx, pointsy)).set_crs(4326)
        # sjoin with roads, to eliminate vertices that don't intersect a road
        road_points.extend(gpd.sjoin(points_gdf, roads_dissolved, op = 'intersects').geometry.values)
    return road_points

def pointarray2matarrays(pointarray):
    """list of points to matlab compatible arrays of longs and lats
    
    i.e.
    [point1, point2,...] -> 'point1_x; point2_x; ...', 'point1_y; point2_y; ...'
    """
    lon = [point.xy[0][0] for point in pointarray]
    lat = [point.xy[1][0] for point in pointarray]
    lon = f"[{'; '.join([str(round(lon, 6)) for lon in list(lon)])}]"
    lat = f"[{'; '.join([str(round(lat, 6)) for lat in list(lat)])}]"
    return lon, lat

# within_points = [get_points_in_roads(g) for g in parcels_sample2.geometry]
road_intersections = parcels_sample2.geometry.progress_apply(get_points_in_roads)

# split into matlab compatible longs and lats like [(longs_list, lats_list), (longs_list, lats_list),...]
road_intersections = [pointarray2matarrays(road_points) for road_points in road_intersections]
lons = [r[0] for r in road_intersections]
lats = [r[1] for r in road_intersections]

parcels_sample2['LINZ_parcel_roadvertices_lon'] = lons
parcels_sample2['LINZ_parcel_roadvertices_lat'] = lats

# example
sample = parcels_sample.iloc[[2065]]
road_points = get_points_in_roads(sample.iloc[0].geometry)
print(road_points)
ax = gpd.GeoDataFrame(geometry=road_points).plot()
sample.plot(ax=ax, alpha=0.5)
roads_dissolved.plot(ax=ax, color='red', alpha=0.5)
x1, y1, x2, y2 = sample.buffer(0.005).total_bounds
plt.xlim(x1, x2)
plt.ylim(y1, y2)
plt.show()

[<shapely.geometry.point.Point object at 0x7fa45980ed90>, <shapely.geometry.point.Point object at 0x7fa45980ea90>, <shapely.geometry.point.Point object at 0x7fa459a8a3a0>, <shapely.geometry.point.Point object at 0x7fa459a506a0>, <shapely.geometry.point.Point object at 0x7fa459a1bbe0>, <shapely.geometry.point.Point object at 0x7fa459a1b220>, <shapely.geometry.point.Point object at 0x7fa459a1b4f0>, <shapely.geometry.point.Point object at 0x7fa459a1b6a0>, <shapely.geometry.point.Point object at 0x7fa459a9c250>, <shapely.geometry.point.Point object at 0x7fa4189409a0>, <shapely.geometry.point.Point object at 0x7fa418940160>, <shapely.geometry.point.Point object at 0x7fa4599f2a90>]

<ipython-input-197-5fad70017631>:8: UserWarning: Geometry is in a geographic CRS. Results from 'buffer' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  x1, y1, x2, y2 = sample.buffer(0.005).total_bounds

h. LINZ_adjoining_parcel_ID id of adjoining LINZ parcels

i. LINZ_parcel_sides_zones AUP Zone Code of adjoining parcels (this includes residential, business, and rural zones; it should also include roads, water and open spaces)

For convenience/speed, 1h and 1i can be gotten simultaneously.
Note: Do 2a and 2b here first, which are needed for 1i.

What to use for joining parcels with AUP zones?

• intersect: fails because a parcel's exterior might intersect with an AUP zone's exterior, without any overlap
• overlap: fails because it is false if one polygon is contained completely within the other
  To use polygon/polygon operations, a combination would be needed; something like intersects but does not touch.
  Better to use a representative point - which may miss cases when a parcel is spread over two AUP zones (not sure if that occurs).
  Bonus: representative point + within is many, many, many times faster than using intersects + not touches (~10s vs not finishing before killing after at least 30 minutes)

first, 2a and 2b

%%time
aup_zones = gpd.read_file('restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_BaseZone.shp')
aup_zones.sindex
# aup_zones = aup_zones.to_crs(2193)
aup_zones.sample(3)

CPU times: user 25.8 s, sys: 2.4 s, total: 28.2 s
Wall time: 30.1 s

	WORKFLOWJO	last_edi00	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	ID	NAME	OBJECTID	PARCEL_BAS	PARCEL_B00	PRECINCT	PRECINCT_r	PRECINCTGR	PRECINCT00	SCHEDULE	SHAPE_Area	SHAPE_Leng	STATUS	SUBPRECINC	SUBPRECI00	SUBTYPE	SUBTYPE_re	TYPE	TYPE_resol	VALIDATION	VALIDATI00	VERSIONSTA	VERSIONS00	ZONE	ZONE_resol	ZONEHEIGHT	geometry
4323	57767.0	20161111010	None	20160718211	None	{2653E2A1-4629-48CA-AADC-2038EFCAC51E}	2	Residential	None	Everglade School	4324	None	None	None	None	None	None	None	25532.898535	745.711232	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	18	Residential - Mixed Housing Suburban Zone	NaN	POLYGON Z ((1768860.282 5903754.262 0.000, 176...
111639	NaN	20161111012	None	20160718211	None	{2AFF46AC-8E35-48EC-89C3-D0E18F776D78}	7	General	None	None	111640	None	None	None	None	None	None	None	3452.179432	398.432755	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	27	Road	NaN	POLYGON Z ((1757930.155 5916271.915 0.000, 175...
47139	NaN	20161111011	None	20160718211	None	{1F572D18-2901-44B3-B7FA-5B9F8F71C99A}	5	Coastal	None	None	47140	None	None	None	None	None	None	None	0.835644	5.790642	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	59	Coastal - Coastal Transition Zone	NaN	POLYGON Z ((1750889.741 5924939.607 0.000, 175...

parcels['geometry'] = parcels.representative_point_4326
parcels = parcels.set_crs(4326)
aup_zones = aup_zones.to_crs(parcels.crs)

%%time
parcels_zoned = gpd.sjoin(parcels, aup_zones[['ZONE', 'ZONE_resol', 'geometry']], op='within').drop(columns=['index_right'])

CPU times: user 9.4 s, sys: 247 ms, total: 9.65 s
Wall time: 9.67 s

# check if there was a one to one match
print(len(parcels_zoned.index.unique()))
print(len(parcels_zoned))
print(len(parcels.index.unique()))
print(len(parcels))

537095
537118
537290
537290

# 23 parcels fall have their representative point fall under multiple AUP zone polygons
# but see below, only one of these falls in two AUP zone polygons with different zone codes
np.unique(parcels_zoned.index.value_counts(), return_counts=True)

(array([1, 2]), array([537072,     23]))

%%time
def get_parcel_zones(id):
    if id in parcels_zoned.index:
        zoned = parcels_zoned.loc[[id]]
        # filter out duplicates
        return ','.join(zoned.ZONE.unique())
    else:
        return None
parcel_zones = process_map(get_parcel_zones, list(parcels.index), max_workers=14, chunksize=1000)

CPU times: user 2.32 s, sys: 1.49 s, total: 3.81 s
Wall time: 43min 3s

# there is a one to one relationship between zones and zone names
print(len(aup_zones[['ZONE', 'ZONE_resol']].drop_duplicates().ZONE.unique()))
print(len(aup_zones[['ZONE', 'ZONE_resol']].drop_duplicates()))
zone2zone_name = {r.ZONE: r.ZONE_resol for _, r in aup_zones[['ZONE', 'ZONE_resol']].drop_duplicates().iterrows()}

50
50

# only one parcel has its representative point fall in two zones
print([z for z in parcel_zones if z is not None and ',' in z])
print(zone2zone_name['18'])
print(zone2zone_name['19'])

['18,19']
Residential - Mixed Housing Suburban Zone
Residential - Single House Zone

# take the first zone where there are multiple (only one case)
parcels['LINZmatch_AUP_code'] = [z.split(',')[0] if (z is not None) else None for z in parcel_zones]
parcels['LINZmatch_AUP_name'] = [zone2zone_name[z.split(',')[0]] if (z is not None) else None for z in parcel_zones]

now do 1h & 1i (now that parcels have zones)

# need polygons, will check for touching neighbours
parcels.geometry = parcels['geometry_polygon_4326']
parcels = parcels.set_crs(4326)

%%time
# number of rows to process at once
n_rows=100
def find_neighbour_info(i):
    """find neighbours of parcels from i * n_rows to (i+1) * n_rows, then find ids and zones of those neighbouring parcels"""
    neighbour_gdf = gpd.sjoin(parcels.iloc[i*n_rows:min((i+1)*n_rows, len(parcels)-1)], parcels, op='touches')
    neighbour_zones = []
    for idx in neighbour_gdf.index:
        neighbour_ids.append(neighbour_gdf.loc[[idx]].id_right.tolist())
        neighbour_zones.append(neighbour_gdf.loc[[idx]].LINZmatch_AUP_code_right.tolist())
    return neighbour_ids, neighbour_zones
        
# each call to find_neighbours will return two lists like this: [list of neighbour ids], [list of niehgbour zones]
# The final structure will be of shape (n_chunks, 2, n_neighbours), where n_neighbours will vary between lists
parcel_neighbour_chunks = process_map(find_neighbour_info, list(range((int(np.ceil(len(parcels) / n_rows))))), max_workers=14, chunksize=10)

parcel_neighbour_chunks

parcels['LINZ_adjoining_parcel_ID'] = [ids for chunk in parcel_neighbour_chunks for ids in chunk[0]]
parcels['LINZ_parcel_sides_zones'] = [zones for chunk in parcel_neighbour_chunks for zones in chunk[1]]

j. i. LINZ_TRNSPWR_ohead_indicator Indicator (0 or 1) for LINZ parcel located under overhead transpower line. Leave blank otherwise. Note that ‘TRANSLINE’ denotes overhead transmission lines in the GIS dataset, while ‘CABLE’ denotes underground transmission lines and can be ignored.

j. ii. LINZ_TRNSPWR_ohead_name

Note: What name to use? 'descriptio'?

power = gpd.read_file('input/Transmission_Lines_exTRANSPOWER/Transmission_Lines.shp')
power = power.cx[bounds2193['x1']:bounds2193['x2'], bounds2193['y1']:bounds2193['y2']]
# only interested in overhead
power = power[power['type'] == 'TRANSLINE']
power.sample(3)

	OBJECTID	MXLOCATION	designvolt	status	descriptio	type	Symbol	Shape__Len	GlobalID	geometry
3	4	ALB-HPI-A	220	COMMISSIONED	Albany - Huapai A	TRANSLINE	220 TRANSLINE	14887.805068	8cb81a31-2c9c-497c-a8fc-646614b9290d	MULTILINESTRING ((1737714.820 5930696.980, 173...
183	194	HLY-OTA-A	220	COMMISSIONED	Huntly - Otahuhu A	TRANSLINE	220 TRANSLINE	76954.696844	43214498-696d-470f-9a06-1592ec8eaab8	MULTILINESTRING ((1790043.000 5842870.000, 179...
83	85	HEN-MPE-A	110	COMMISSIONED	Henderson - Maungatapere A	TRANSLINE	110 TRANSLINE	137361.615225	8c723fe3-75ae-487c-9e8f-d6431a1bc9d4	MULTILINESTRING ((1744228.000 5921585.000, 174...

ax = power.plot(column='designvolt', legend=True)
plt
ctx.add_basemap(ax, source=ctx.providers.Esri.WorldImagery, crs=2193)

parcels_sample['geometry'] = parcels_sample['geometry_polygon_2193']
parcels_sample = parcels_sample.set_crs(2193)
# get dataframe associating parcel indices with overhead power lines
power_intersect = gpd.sjoin(parcels_sample, power[['descriptio', 'geometry']]).drop(columns=['index_right'])

power_intersect.index.value_counts()

377815    10
381419     3
403504     3
466685     2
2097       2
          ..
333894     1
329285     1
169028     1
371011     1
45696      1
Length: 100, dtype: int64

id = 381419
ax = parcels_sample_under.loc[id].plot()
bounds = parcels_sample_under.loc[id].iloc[[0]].bounds
plt.xlim((bounds.minx.values[0] - 100, bounds.maxx.values[0] + 100))
plt.ylim((bounds.miny.values[0] - 100, bounds.maxy.values[0] + 100))
power.cx[bounds.minx.values[0]:bounds.maxx.values[0], bounds.miny.values[0]:bounds.maxy.values[0]].plot(column='descriptio', ax = ax, legend=True)

<AxesSubplot:>

%%time
def get_powerlines(id):
    if id in power_intersect.index:
        powerlines = power_intersect.loc[[id]]
        # filter out duplicates
        return powerlines.descriptio.unique().tolist()
#         return ','.join(powerlines.descriptio.unique())

    else:
        return None
parcel_powerlines = process_map(get_powerlines, list(parcels_sample.index), max_workers=14, chunksize=1000)
parcels_sample['LINZ_TRNSPWR_ohead_name'] = parcel_powerlines
parcels_sample['LINZ_TRNSPWR_ohead_indicator'] = [p is not None for p in parcel_powerlines]

CPU times: user 95.4 ms, sys: 1.45 s, total: 1.55 s
Wall time: 1.86 s

j. i. LINZ_VWSHFT_ohead_indicator Indicator (0 or 1) for LINZ parcel located under viewshafts. Leave blank otherwise.

j. ii. LINZ_VWSHFT_ohead_name Name of the volcanic cone (e.g. Mt Albert). Leave blank if no viewshaft applies.

j. iii. LINZ_VWSHFT_ohead_ID OBJECTID of the viewshaft. Leave blank if no viewshaft applies.

!ls restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/*View[Ss]haf*.shp

restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_AucklandMuseumViewshaftContoursOverlay.shp
restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_AucklandMuseumViewshaftOverlay.shp
restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_DilworthTerraceHousesViewshaftContoursOverlay.shp
restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_DilworthTerraceHousesViewshaftOverlay.shp
restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_LocallySignificantVolcanicViewshaftsContours.shp
restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_LocallySignificantVolcanicViewshafts.shp
restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_RegionallySignificantVolcanicViewShaftsAndHeightSensitiveAreasOverlay.shp
restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_RegionallySignificantVolcanicViewshaftsContours.shp

!ls restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/*ViewShaf*.shp

restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_RegionallySignificantVolcanicViewShaftsAndHeightSensitiveAreasOverlay.shp

viewshafts_local = gpd.read_file('restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_LocallySignificantVolcanicViewshafts.shp').to_crs(2193)
viewshafts_regional = gpd.read_file('restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_RegionallySignificantVolcanicViewShaftsAndHeightSensitiveAreasOverlay.shp').to_crs(2193)

ax = viewshafts_regional[viewshafts_regional.NAME.isin(viewshafts_local.NAME.unique())].plot(color='blue', alpha=0.6)
viewshafts_local.plot(ax=ax, color='red', alpha=0.6)
blue_patch = mpatches.Patch(color='blue', label='regionally significant viewshafts')
red_patch = mpatches.Patch(color='red', label='locally significant viewshafts')
plt.legend(handles=[red_patch, blue_patch])
ctx.add_basemap(ax, source=ctx.providers.Esri.WorldImagery, crs=2193)

ax = viewshafts_local.plot(column='NAME', alpha=0.6, legend=True)
ctx.add_basemap(ax, source=ctx.providers.Esri.WorldImagery, crs=2193)

ax = viewshafts_regional.plot(column='NAME', alpha=0.6, legend=True)
ctx.add_basemap(ax, source=ctx.providers.Esri.WorldImagery, crs=2193)

viewshafts_museum = gpd.read_file('restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_AucklandMuseumViewshaftOverlay.shp').to_crs(2193)
viewshafts_dilworth = gpd.read_file('restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_DilworthTerraceHousesViewshaftOverlay.shp').to_crs(2193)

ax = viewshafts_museum.plot(alpha=0.6, legend=True)
ctx.add_basemap(ax, source=ctx.providers.Esri.WorldImagery, crs=2193)

ax = viewshafts_dilworth.plot(alpha=0.6, legend=True)
ctx.add_basemap(ax, source=ctx.providers.Esri.WorldImagery, crs=2193)

parcels_sample['geometry'] = parcels_sample.geometry_polygon_2193
parcels_sample = parcels_sample.set_crs(2193)
gpd.sjoin(parcels_sample, viewshafts_regional, how='left')

	id	appellation	affected_surveys	parcel_intent	topology_type	statutory_actions	land_district	titles	survey_area	calc_area	geometry	geometry_centroid_4326	geometry_polygon_4326	geometry_centroid_2193	geometry_polygon_2193	LINZ_parcel_vertices_lon	LINZ_parcel_vertices_lat	index_right	OBJECTID	TYPE	TYPE_resol	SUBTYPE	SCHEDULE	NAME	VALIDATION	VALIDATI00	last_edi00	created_da	VERSIONSTA	VERSIONS00	DocumentUR	GlobalID	SHAPE_Leng	SHAPE_Area
332148	6611744	Section 15 SO 317214	SO 317214	Road	Primary	[Create] Road, Limited Access Road and State H...	North Auckland	None	594.0	600.0	MULTIPOLYGON (((1749971.336 5940738.679, 17499...	POINT (174.67774 -36.66742)	MULTIPOLYGON (((174.67817 -36.66715, 174.67822...	POINT (1749932.255 5940710.038)	MULTIPOLYGON (((1749971.336 5940738.679, 17499...	[174.678168; 174.678224; 174.677304; 174.67725...	[-36.667152; -36.667189; -36.667681; -36.66764...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
138662	4971220	Lot 71 DP 4276	DP 4276	DCDB	Primary	None	North Auckland	NA184/150	612.0	613.0	MULTIPOLYGON (((1755077.168 5915667.766, 17550...	POINT (174.74041 -36.89243)	MULTIPOLYGON (((174.74038 -36.89224, 174.74055...	POINT (1755079.119 5915646.345)	MULTIPOLYGON (((1755077.168 5915667.766, 17550...	[174.74038; 174.740546; 174.740433; 174.740267...	[-36.892238; -36.892272; -36.892623; -36.89258...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
276820	6555480	Lot 2 DP 204937	DP 204937	Fee Simple Title	Primary	None	North Auckland	NA133B/442	475.0	475.0	MULTIPOLYGON (((1771091.292 5912435.935, 17710...	POINT (174.92060 -36.91862)	MULTIPOLYGON (((174.92074 -36.91859, 174.92076...	POINT (1771078.208 5912432.399)	MULTIPOLYGON (((1771091.292 5912435.935, 17710...	[174.920744; 174.920757; 174.920695; 174.92073...	[-36.918588; -36.91862; -36.918719; -36.918737...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
31004	4744873	Lot 106 DP 57498	DP 57498	DCDB	Primary	None	North Auckland	NA11B/1184	657.0	657.0	MULTIPOLYGON (((1751708.537 5925603.069, 17517...	POINT (174.70078 -36.80340)	MULTIPOLYGON (((174.70061 -36.80326, 174.70078...	POINT (1751723.367 5925587.744)	MULTIPOLYGON (((1751708.537 5925603.069, 17517...	[174.700606; 174.70078; 174.700945; 174.700846...	[-36.803262; -36.803205; -36.803533; -36.80356...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
494255	4696478	Lot 49 DP 71916	DP 71916	DCDB	Primary	None	North Auckland	NA38A/689	675.0	675.0	MULTIPOLYGON (((1755689.311 5929951.217, 17557...	POINT (174.74443 -36.76359)	MULTIPOLYGON (((174.74432 -36.76344, 174.74465...	POINT (1755698.853 5929934.142)	MULTIPOLYGON (((1755689.311 5929951.217, 17557...	[174.744322; 174.744652; 174.744543; 174.74421...	[-36.763436; -36.763582; -36.763741; -36.76359...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
24891	4732360	Lot 1 DP 189392	DP 189392	DCDB	Primary	None	North Auckland	NA118B/374	860.0	793.0	MULTIPOLYGON (((1733103.033 5930453.056, 17331...	POINT (174.49143 -36.76224)	MULTIPOLYGON (((174.49127 -36.76235, 174.49127...	POINT (1733117.978 5930465.529)	MULTIPOLYGON (((1733103.033 5930453.056, 17331...	[174.491265; 174.491267; 174.491269; 174.49126...	[-36.762354; -36.762346; -36.762331; -36.76231...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
25636	4733918	Lot 105 DP 59567	DP 59567	DCDB	Primary	None	North Auckland	NA14C/462	703.0	703.0	MULTIPOLYGON (((1770070.630 5918154.599, 17700...	POINT (174.90802 -36.86744)	MULTIPOLYGON (((174.90801 -36.86725, 174.90821...	POINT (1770070.878 5918133.805)	MULTIPOLYGON (((1770070.630 5918154.599, 17700...	[174.908008; 174.908208; 174.908026; 174.90782...	[-36.867249; -36.867551; -36.867628; -36.86732...	5.0	6.0	3	Height Sensitive Areas	None	None	None	3	Valid and Public	20161109010	20160530235	4	Operative	None	{7EE3FD70-DD2B-4138-B975-6CA1301626A8}	9595.090952	1.303548e+06
25636	4733918	Lot 105 DP 59567	DP 59567	DCDB	Primary	None	North Auckland	NA14C/462	703.0	703.0	MULTIPOLYGON (((1770070.630 5918154.599, 17700...	POINT (174.90802 -36.86744)	MULTIPOLYGON (((174.90801 -36.86725, 174.90821...	POINT (1770070.878 5918133.805)	MULTIPOLYGON (((1770070.630 5918154.599, 17700...	[174.908008; 174.908208; 174.908026; 174.90782...	[-36.867249; -36.867551; -36.867628; -36.86732...	6.0	7.0	4	Viewshafts	None	B6	Browns Island	3	Valid and Public	20161109010	20160531002	4	Operative	None	{4921E0E2-AFE9-4BAA-9D17-CC5669FC65C1}	31274.305310	3.767934e+07
369084	7503911	Part Lot 33 DP 19195	None	Fee Simple Title	Primary	None	North Auckland	NA46C/131	556.0	7.0	MULTIPOLYGON (((1753477.305 5920162.246, 17534...	POINT (174.72155 -36.85201)	MULTIPOLYGON (((174.72152 -36.85200, 174.72156...	POINT (1753479.593 5920160.987)	MULTIPOLYGON (((1753477.305 5920162.246, 17534...	[174.721524; 174.721557; 174.721568; 174.721524]	[-36.852002; -36.851997; -36.85204; -36.852002]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
334475	4769767	Part Allot N35 PSH OF Ahuroa	SO 27	DCDB	Primary	None	North Auckland	NA14D/470	730609.0	120330.0	MULTIPOLYGON (((1743070.851 5963605.949, 17430...	POINT (174.59335 -36.46327)	MULTIPOLYGON (((174.59673 -36.46215, 174.59668...	POINT (1742765.292 5963486.442)	MULTIPOLYGON (((1743070.851 5963605.949, 17430...	[174.596732; 174.596678; 174.594612; 174.58897...	[-36.462145; -36.464002; -36.464061; -36.46422...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

10527 rows × 34 columns

def extract_viewshaft_info(

parcels_sample.geometry.iloc[0].overlaps()

<bound method BaseGeometry.overlaps of <shapely.geometry.multipolygon.MultiPolygon object at 0x7f8c3ef45fa0>>

2. AUP shapefile information. Calculations are based on centroid of matched parcels:

a. LINZmatch_AUP_name AUP zone (string) consent is located in (from the AUP shapefiles)

b. LINZmatch_AUP_code AUP Zone Code (numerical) consent is located in

Done above in 1h and 1i.

c. Hdist_rural Minimum Haversine distance to nearest Rural Zone, plus

c. i. Hdist_rural_name Name of rural zone identified

c. ii. Hdist_rural_code Numeric code of rural zone identified

I've included all of these as rural:
['Rural - Mixed Rural Zone',
'Rural - Rural Coastal Zone',
'Rural - Countryside Living Zone',
'Rural - Rural Production Zone',
'Rural - Rural Conservation Zone',
'Rural - Waitakere Ranges Zone',
'Rural - Waitakere Foothills Zone']

%%time
aup_zones = gpd.read_file('restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_BaseZone.shp')
aup_zones = aup_zones.to_crs(2193)
aup_zones.sample(3)

CPU times: user 31.2 s, sys: 2.21 s, total: 33.4 s
Wall time: 33.3 s

	WORKFLOWJO	last_edi00	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	ID	NAME	OBJECTID	PARCEL_BAS	PARCEL_B00	PRECINCT	PRECINCT_r	PRECINCTGR	PRECINCT00	SCHEDULE	SHAPE_Area	SHAPE_Leng	STATUS	SUBPRECINC	SUBPRECI00	SUBTYPE	SUBTYPE_re	TYPE	TYPE_resol	VALIDATION	VALIDATI00	VERSIONSTA	VERSIONS00	ZONE	ZONE_resol	ZONEHEIGHT	geometry
58724	NaN	20161111011	None	20160718211	None	{0CCC66B0-9A15-477A-BE4F-FAE9C5FA9C15}	5	Coastal	None	None	58725	None	None	None	None	None	None	None	62.153950	58.346375	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	59	Coastal - Coastal Transition Zone	NaN	POLYGON Z ((1756089.130 5973206.276 0.000, 175...
103279	NaN	20161111012	None	20160718211	None	{111B7716-6401-457F-A260-9BA18D7E0DB9}	5	Coastal	None	None	103280	None	None	None	None	None	None	None	372.662968	106.669373	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	30	Coastal - General Coastal Marine Zone	NaN	POLYGON Z ((1810661.036 5993908.378 0.000, 181...
54081	NaN	20161111011	None	20160718211	None	{CD16D435-B3A5-4C26-BFC7-E6AF32E60231}	7	General	None	None	54082	None	None	None	None	None	None	None	41502.960080	1179.183755	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	26	Strategic Transport Corridor Zone	NaN	POLYGON Z ((1737071.120 5973948.034 0.000, 173...

rural_codes = aup_zones[aup_zones.ZONE_resol.str.lower().str.contains('rural - ', na=False)]['ZONE'].unique()

# check that each rural zone code matches with a unique rural zone name 
assert all([len(aup_zones[aup_zones.ZONE == code].ZONE_resol.unique()) == 1 for code in rural_codes])
# dictionary mapping code to names
rural_code2name = {code: aup_zones[aup_zones.ZONE == code].ZONE_resol.unique()[0] for code in rural_codes}

aup_zones[aup_zones.ZONE_resol.isna()]

	WORKFLOWJO	last_edi00	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	ID	NAME	OBJECTID	PARCEL_BAS	PARCEL_B00	PRECINCT	PRECINCT_r	PRECINCTGR	PRECINCT00	SCHEDULE	SHAPE_Area	SHAPE_Leng	STATUS	SUBPRECINC	SUBPRECI00	SUBTYPE	SUBTYPE_re	TYPE	TYPE_resol	VALIDATION	VALIDATI00	VERSIONSTA	VERSIONS00	ZONE	ZONE_resol	ZONEHEIGHT	geometry
20853	NaN	20161111011	None	20160718211	None	{2D83F680-9587-4D57-AF0B-CB7CA27C3D2C}	6	Special purpose zone	None	None	20854	None	None	None	None	None	None	None	22.286945	72.629617	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	58	None	NaN	POLYGON Z ((1767684.860 5903714.023 33.721, 17...
121765	NaN	20161111010	None	20160718211	None	{FD3E8BB4-1979-42B5-9A77-5D04AE5190AF}	6	Special purpose zone	None	None	121766	None	None	None	None	None	None	None	4.114962	86.135341	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	58	None	NaN	POLYGON Z ((1767684.083 5903713.690 33.721, 17...

# 2 NAs in ZONE_resol are from a zone 58, which only has observations
aup_zones[aup_zones.ZONE == '58']

	WORKFLOWJO	last_edi00	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	ID	NAME	OBJECTID	PARCEL_BAS	PARCEL_B00	PRECINCT	PRECINCT_r	PRECINCTGR	PRECINCT00	SCHEDULE	SHAPE_Area	SHAPE_Leng	STATUS	SUBPRECINC	SUBPRECI00	SUBTYPE	SUBTYPE_re	TYPE	TYPE_resol	VALIDATION	VALIDATI00	VERSIONSTA	VERSIONS00	ZONE	ZONE_resol	ZONEHEIGHT	geometry
20853	NaN	20161111011	None	20160718211	None	{2D83F680-9587-4D57-AF0B-CB7CA27C3D2C}	6	Special purpose zone	None	None	20854	None	None	None	None	None	None	None	22.286945	72.629617	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	58	None	NaN	POLYGON Z ((1767684.860 5903714.023 33.721, 17...
121765	NaN	20161111010	None	20160718211	None	{FD3E8BB4-1979-42B5-9A77-5D04AE5190AF}	6	Special purpose zone	None	None	121766	None	None	None	None	None	None	None	4.114962	86.135341	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	58	None	NaN	POLYGON Z ((1767684.083 5903713.690 33.721, 17...

rural = aup_zones[aup_zones.ZONE.isin(rural_codes)]

%%time
# method 1, slightly slower: dissolve by zone
rural_by_zone = rural.dissolve(by='ZONE').reset_index(drop=False)
distances = []
codes = []
for index, row in tqdm(parcels_sample.iterrows(), total=len(parcels_sample)):
    distance_candidates = []
    code_candidates = []
    for index, rural_row in rural_by_zone.iterrows():
        distance_candidates.append(row.geometry.distance(rural_row.geometry))
        code_candidates.append(rural_row.ZONE)
    distances.append(distance_candidates)
    codes.append(code_candidates)
    
# all distances (to any zone)
distances = np.array(distances)
codes = np.array(codes)
# indices of minimum distances
min_idx = np.argmin(distances, axis=-1)

CPU times: user 1min 2s, sys: 363 ms, total: 1min 3s
Wall time: 1min 2s

%%time
# method 2, slightly faster: dissolve all after subsetting gpd dfs
rural_by_zone_dict = {code: rural[rural.ZONE == code].dissolve() for code in rural_codes}

distances = []
codes = []
for index, row in tqdm(parcels_sample.iterrows(), total=len(parcels_sample)):
    distance_candidates = []
    code_candidates = []
    for code, rural_gdf in rural_by_zone_dict.items():
        distance_candidates.append(row.geometry.distance(rural_gdf.geometry[0]))
        code_candidates.append(rural_gdf.ZONE[0])
    distances.append(distance_candidates)
    codes.append(code_candidates)

# all distances (to any zone)
distances = np.array(distances)
codes = np.array(codes)
# indices of minimum distances
min_idx = np.argmin(distances, axis=-1)

CPU times: user 56.2 s, sys: 289 ms, total: 56.5 s
Wall time: 56.2 s

parcels_sample['Hdist_rural'] = distances[(np.arange(len(distances)), min_idx)]
parcels_sample['Hdist_rural_code'] = codes[(np.arange(len(distances)), min_idx)]
parcels_sample['Hdist_rural_name'] = parcels_sample.apply(lambda x: rural_code2name[x.Hdist_rural_code], axis=1)

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt

colours = ('blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'cyan')
name2colour = {name: colour for name, colour in zip(rural_code2name.values(), colours)}

column = 'Hdist_rural'
subsample = parcels_sample[parcels_sample[column] > 0].sample(5)
subsample['buffer'] = subsample.apply(lambda x: x.geometry.buffer(x[column]), axis=1)
subsample['geometry'] = subsample['buffer']
subsample = subsample[~subsample.is_empty]
# ax = rural.plot(column='ZONE_resol', legend=True)
# subsample.plot(column='Hdist_rural_name', alpha=0.4, ax=ax)
ax = rural.plot(color=[name2colour[z] for z in rural.ZONE_resol])
subsample.plot(color=[name2colour[z] for z in subsample.Hdist_rural_name], alpha=0.65, ax=ax)
plt.ylim((5.88e6, 5.96e6))

# add legend
legend_patches = [mpatches.Patch(color=col, label=name) for name, col in name2colour.items()]
plt.legend(handles=legend_patches)

ctx.add_basemap(ax=ax, crs=2193)

import matplotlib
matplotlib.colors

<module 'matplotlib.colors' from '/data/miniconda3/envs/house-upzone/lib/python3.8/site-packages/matplotlib/colors.py'>

d. Hdist_bus Minimum Haversine distance to nearest Business Zone

d. i. Hdist_bus_name

d. ii. Hdist_bus_code

business_codes = aup_zones[aup_zones.ZONE_resol.str.lower().str.contains('business - ', na=False)]['ZONE'].unique()

# check that each business zone code matches with a unique business zone name 
assert all([len(aup_zones[aup_zones.ZONE == code].ZONE_resol.unique()) == 1 for code in business_codes])
# dictionary mapping code to names
business_code2name = {code: aup_zones[aup_zones.ZONE == code].ZONE_resol.unique()[0] for code in business_codes}
business_code2name

{'44': 'Business - Neighbourhood Centre Zone',
 '12': 'Business - Mixed Use Zone',
 '17': 'Business - Light Industry Zone',
 '5': 'Business - Heavy Industry Zone',
 '49': 'Business - General Business Zone',
 '1': 'Business - Business Park Zone',
 '22': 'Business - Town Centre Zone',
 '10': 'Business - Metropolitan Centre Zone',
 '7': 'Business - Local Centre Zone',
 '35': 'Business - City Centre Zone'}

business = aup_zones[aup_zones.ZONE.isin(business_codes)]

%%time
# method 1, slightly slower: dissolve by zone
business_by_zone = business.dissolve(by='ZONE').reset_index(drop=False)
distances = []
codes = []
for index, row in tqdm(parcels_sample.iterrows(), total=len(parcels_sample)):
    distance_candidates = []
    code_candidates = []
    for index, business_row in business_by_zone.iterrows():
        distance_candidates.append(row.geometry.distance(business_row.geometry))
        code_candidates.append(business_row.ZONE)
    distances.append(distance_candidates)
    codes.append(code_candidates)
    
# all distances (to any zone)
distances = np.array(distances)
codes = np.array(codes)
# indices of minimum distances
min_idx = np.argmin(distances, axis=-1)

CPU times: user 1min 2s, sys: 363 ms, total: 1min 3s
Wall time: 1min 2s

%%time
# method 2, slightly faster: dissolve all after subsetting gpd dfs
business_by_zone_dict = {code: business[business.ZONE == code].dissolve() for code in business_codes}

distances = []
codes = []
for index, row in tqdm(parcels_sample.iterrows(), total=len(parcels_sample)):
    distance_candidates = []
    code_candidates = []
    for code, business_gdf in business_by_zone_dict.items():
        distance_candidates.append(row.geometry.distance(business_gdf.geometry[0]))
        code_candidates.append(business_gdf.ZONE[0])
    distances.append(distance_candidates)
    codes.append(code_candidates)

# all distances (to any zone)
distances = np.array(distances)
codes = np.array(codes)
# indices of minimum distances
min_idx = np.argmin(distances, axis=-1)

CPU times: user 44.9 s, sys: 230 ms, total: 45.1 s
Wall time: 44.8 s

parcels_sample['Hdist_bus'] = distances[(np.arange(len(distances)), min_idx)]
parcels_sample['Hdist_bus_code'] = codes[(np.arange(len(distances)), min_idx)]
parcels_sample['Hdist_bus_name'] = parcels_sample.apply(lambda x: business_code2name[x.Hdist_bus_code], axis=1)

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt

colours = ('blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'cyan', 'black', 'white')
name2colour = {name: colour for name, colour in zip(business_code2name.values(), colours)}

# hard to see, subset to smaller area
bounds = {'x1': 1.76e6, 'x2': 1.7613e6, 'y1': 5.9123e6, 'y2': 5.914e6}

column = 'Hdist_bus'
subsample = parcels_sample.cx[bounds['x1']:bounds['x2'], bounds['y1']:bounds['y2']]
subsample['buffer'] = subsample.apply(lambda x: x.geometry.buffer(x[column]), axis=1)
subsample['geometry'] = subsample['buffer']

subsample = subsample[~subsample.is_empty]

business_plot = business.cx[bounds['x1']:bounds['x2'], bounds['y1']:bounds['y2']]
ax = business_plot.plot(color=[name2colour[z] for z in business_plot.ZONE_resol])
subsample.plot(color=[name2colour[z] for z in subsample.Hdist_bus_name], alpha=0.65, ax=ax)
# plt.ylim((5.88e6, 5.96e6))

# add legend
legend_patches = [mpatches.Patch(color=col, label=name) for name, col in name2colour.items()]
plt.legend(handles=legend_patches)

ctx.add_basemap(ax=ax, crs=2193)

/data/miniconda3/envs/house-upzone/lib/python3.8/site-packages/geopandas/geodataframe.py:1322: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super(GeoDataFrame, self).__setitem__(key, value)

e. Hdist_resid Minimum Haversine distance to nearest Residential Zone,

e. i. Hdist_resid_name

e. ii. Hdist_resid_code

resid_codes = aup_zones[aup_zones.ZONE_resol.str.lower().str.contains('resid', na=False)]['ZONE'].unique()

resid_codes

array(['60', '23', '18', '8', '19', '20'], dtype=object)

# check that each resid zone code matches with a unique resid zone name 
assert all([len(aup_zones[aup_zones.ZONE == code].ZONE_resol.unique()) == 1 for code in resid_codes])
# dictionary mapping code to names
resid_code2name = {code: aup_zones[aup_zones.ZONE == code].ZONE_resol.unique()[0] for code in resid_codes}
resid_code2name

{'60': 'Residential - Mixed Housing Urban Zone',
 '23': 'Residential - Large Lot Zone',
 '18': 'Residential - Mixed Housing Suburban Zone',
 '8': 'Residential - Terrace Housing and Apartment Building Zone',
 '19': 'Residential - Single House Zone',
 '20': 'Residential - Rural and Coastal Settlement Zone'}

resid = aup_zones[aup_zones.ZONE.isin(resid_codes)]

%%time
# method 1, slightly slower: dissolve by zone
resid_by_zone = resid.dissolve(by='ZONE').reset_index(drop=False)
distances = []
codes = []
for index, row in tqdm(parcels_sample.iterrows(), total=len(parcels_sample)):
    distance_candidates = []
    code_candidates = []
    for index, resid_row in resid_by_zone.iterrows():
        distance_candidates.append(row.geometry.distance(resid_row.geometry))
        code_candidates.append(resid_row.ZONE)
    distances.append(distance_candidates)
    codes.append(code_candidates)
    
# all distances (to any zone)
distances = np.array(distances)
codes = np.array(codes)
# indices of minimum distances
min_idx = np.argmin(distances, axis=-1)

CPU times: user 1min 2s, sys: 363 ms, total: 1min 3s
Wall time: 1min 2s

%%time
# method 2, slightly faster: dissolve all after subsetting gpd dfs
resid_by_zone_dict = {code: resid[resid.ZONE == code].dissolve() for code in resid_codes}

distances = []
codes = []
for index, row in tqdm(parcels_sample.iterrows(), total=len(parcels_sample)):
    distance_candidates = []
    code_candidates = []
    for code, resid_gdf in resid_by_zone_dict.items():
        distance_candidates.append(row.geometry.distance(resid_gdf.geometry[0]))
        code_candidates.append(resid_gdf.ZONE[0])
    distances.append(distance_candidates)
    codes.append(code_candidates)

# all distances (to any zone)
distances = np.array(distances)
codes = np.array(codes)
# indices of minimum distances
min_idx = np.argmin(distances, axis=-1)

CPU times: user 2min 9s, sys: 509 ms, total: 2min 9s
Wall time: 2min 8s

parcels_sample['Hdist_resid'] = distances[(np.arange(len(distances)), min_idx)]
parcels_sample['Hdist_resid_code'] = codes[(np.arange(len(distances)), min_idx)]
parcels_sample['Hdist_resid_name'] = parcels_sample.apply(lambda x: resid_code2name[x.Hdist_resid_code], axis=1)

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt

colours = ('blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'cyan', 'black', 'white')
name2colour = {name: colour for name, colour in zip(resid_code2name.values(), colours)}

# hard to see, subset to smaller area
bounds = {'x1': 1.76e6, 'x2': 1.7613e6, 'y1': 5.9123e6, 'y2': 5.914e6}

column = 'Hdist_resid'
subsample = parcels_sample.cx[bounds['x1']:bounds['x2'], bounds['y1']:bounds['y2']]
subsample['buffer'] = subsample.apply(lambda x: x.geometry.buffer(x[column]), axis=1)
subsample['geometry'] = subsample['buffer']

subsample = subsample[~subsample.is_empty]

resid_plot = resid.cx[bounds['x1']:bounds['x2'], bounds['y1']:bounds['y2']]
ax = resid_plot.plot(color=[name2colour[z] for z in resid_plot.ZONE_resol])
subsample.plot(color=[name2colour[z] for z in subsample.Hdist_resid_name], alpha=0.65, ax=ax)
# plt.ylim((5.88e6, 5.96e6))

# add legend
legend_patches = [mpatches.Patch(color=col, label=name) for name, col in name2colour.items()]
plt.legend(handles=legend_patches)

ctx.add_basemap(ax=ax, crs=2193)

/data/miniconda3/envs/house-upzone/lib/python3.8/site-packages/geopandas/geodataframe.py:1322: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super(GeoDataFrame, self).__setitem__(key, value)

f. Hdist_SH Minimum Haversine distance to Residential - Single House Zone

g. Hdist_MHS Minimum Haversine distance to Residential - Mixed Housing Suburban Zone

h. Hdist_MHU Minimum Haversine distance to Residential - Mixed Housing Urban Zone

i. Zone Hdist_THA Minimum Haversine distance to Residential - Terrace Housing and Apartments

Note: this is the real name for i: 'Residential - Terrace Housing and Apartment Building Zone'

%%time
postfix2name = {
            'SH': 'Residential - Single House Zone',
            'MHS': 'Residential - Mixed Housing Suburban Zone',
            'MHU': 'Residential - Mixed Housing Urban Zone',
            'THA': 'Residential - Terrace Housing and Apartment Building Zone'
               }
for postfix, zone in tqdm(postfix2name.items()):
    resid_gdf = resid[resid.ZONE_resol == zone].dissolve()
    parcels_sample[f'Hdist_{postfix}'] = parcels_sample.apply(lambda x: x.geometry.distance(resid_gdf.geometry[0]), axis=1)

CPU times: user 1min 42s, sys: 78.1 ms, total: 1min 42s
Wall time: 1min 42s

postfix = 'THA'
column = f'Hdist_{postfix}'
subsample = parcels_sample.sample(10)
subsample['buffer'] = subsample.apply(lambda x: x.geometry.buffer(x[column]), axis=1)
subsample['geometry'] = subsample['buffer']
ax = subsample.plot(color='red', alpha=0.4)
resid[resid.ZONE_resol == postfix2name[postfix]].plot(ax=ax)
ctx.add_basemap(ax, crs=2193)

3. Local Area (LA) Information (information on the LA that the consent is located in):

a. Name (just the string is fine; I did not generate numeric codes for the local areas generated) Local_Area_name

LA = gpd.read_file('input/Modified_Community_Boards_SHP.zip').to_crs(2193)
LA.sample(3)

	OBJECTID	Local_Area	geometry
8	10.0	Hibiscus Coast	MULTIPOLYGON (((1752023.352 5954803.281, 17520...
17	21.0	Papatoetoe	POLYGON ((1765105.421 5908611.893, 1765113.661...
2	25.0	Rodney-Kumeu-Riverhead	POLYGON ((1742532.808 5931237.574, 1742490.377...

parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample = gpd.sjoin(parcels_sample, LA[['Local_Area', 'geometry']]).drop(columns=['index_right'])
parcels_sample = parcels_sample.rename(columns={'Local_Area': 'Local_Area_name'})

4. 2018 Statistical Area 2 Information (information on the 2018SA2 that the consent is located in)

a. Name (string) SA22018_name

b. Numeric code SA22018_code

sa2 = gpd.read_file('NZ-SA/statistical-area-2-2020-generalised.gdb').to_crs(2193)
sa2.sample(3)

%%time
parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample = gpd.sjoin(parcels_sample, sa2[['SA22020_V1_00_NAME', 'SA22020_V1_00', 'geometry']]).drop(columns=['index_right'])
parcels_sample = parcels_sample.rename(columns={'SA22020_V1_00_NAME': 'SA22018_name', 'SA22020_V1_00': 'SA22018_code'})

CPU times: user 8.29 s, sys: 3.88 ms, total: 8.29 s
Wall time: 8.29 s

5. Area Unit Information (information on the 2013AU  that the consent is located in):

a. Name (string) AU2013_name

b. Numeric code AU2013_code

au2013 = gpd.read_file('input/area-unit-2013.gdb.zip').to_crs(2193)
au2013.sample(3)

	AU2013_V1_00	AU2013_V1_00_NAME	AREA_SQ_KM	LAND_AREA_SQ_KM	Shape_Length	geometry
1481	573903	Newlands North	0.804087	0.804087	5958.640337	MULTIPOLYGON (((174.82896 -41.21949, 174.83011...
958	525202	Kawakawa-Orere	105.216001	105.216001	57437.556145	MULTIPOLYGON (((175.14259 -36.93214, 175.14265...
608	597102	Inland Water-Lake Ellesmere South	63.304671	0.000000	75233.330853	MULTIPOLYGON (((172.57398 -43.76779, 172.57401...

%%time
parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample = gpd.sjoin(parcels_sample, au2013[['AU2013_V1_00_NAME', 'AU2013_V1_00', 'geometry']]).drop(columns=['index_right'])
parcels_sample = parcels_sample.rename(columns={'AU2013_V1_00_NAME': 'AU2013_name', 'AU2013_V1_00': 'AU2013_code'})

CPU times: user 6.96 s, sys: 0 ns, total: 6.96 s
Wall time: 6.96 s

6. 2018 Meshblock Information (information on the 2018MB  that the consent is located in):

a. Code MB2018_code

mb2018 = gpd.read_file('input/meshblock-2018-clipped-generalised.gdb.zip').to_crs(4326)
mb2018.sample(3)

	MB2018_V1_00	LANDWATER	LANDWATER_NAME	LAND_AREA_SQ_KM	AREA_SQ_KM	SHAPE_Length	geometry
52865	4011926	12	Mainland	0.173710	0.173710	2129.422679	MULTIPOLYGON (((174.91945 -36.94519, 174.92053...
23832	1429800	12	Mainland	0.021918	0.021918	731.394363	MULTIPOLYGON (((176.91682 -39.49291, 176.91702...
15554	0759520	12	Mainland	0.020695	0.020695	928.355735	MULTIPOLYGON (((174.91840 -37.01411, 174.91871...

%%time
parcels_sample['geometry'] = parcels_sample['geometry_centroid']
parcels_sample = gpd.sjoin(parcels_sample, mb2018[['MB2018_V1_00', 'geometry']]).drop(columns=['index_right'])
parcels_sample = parcels_sample.rename(columns={'MB2018_V1_00': 'MB2018_code'})

CPU times: user 11.3 s, sys: 8.2 ms, total: 11.3 s
Wall time: 11.3 s

7. 2013 Meshblock Information (information on the 2013MB  that the consent is located in):

a. Code MB2013_code

mb2013 = gpd.read_file('input/meshblock-2013.gdb.zip').to_crs(4326)
mb2013.sample(3)

%%time
parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample = gpd.sjoin(parcels_sample, mb2013[['MeshblockNumber', 'geometry']]).drop(columns=['index_right'])
parcels_sample = parcels_sample.rename(columns={'MeshblockNumber': 'MB2013_code'})

CPU times: user 11.4 s, sys: 7.05 ms, total: 11.4 s
Wall time: 11.4 s

8. Additional distance information from consent location

For these distance calculations, use EPSG 2193 (less distortion).

parcels_sample['geometry'] = parcels_sample['geometry_centroid']
parcels_sample = parcels_sample.to_crs(2193)
parcels_sample.sample(3)

	id	appellation	affected_surveys	parcel_intent	topology_type	statutory_actions	land_district	titles	survey_area	calc_area	geometry	geometry_centroid	geometry_polygon	SA22018_name	SA22018_code	MB2013_code	MB2018_code	AU2013_name	AU2013_code
102782	4896031	Lot 1 DP 61837	DP 61837	DCDB	Primary	None	North Auckland	NA17C/935	685.0	685.0	POINT (1773608.374 5914596.096)	POINT (174.94849 -36.89867)	MULTIPOLYGON (((174.94827 -36.89874, 174.94841...	Cockle Bay	153400	0655102	0655102	Cockle Bay	521502
119786	4931702	Lot 4 DP 166054	DP 166054	DCDB	Primary	None	North Auckland	NA100D/34	52130.0	50575.0	POINT (1783027.533 5889659.615)	POINT (175.06020 -37.12153)	MULTIPOLYGON (((175.06295 -37.12105, 175.06293...	Ararimu	166400	0815302	0815302	Hunua	521132
362342	7415715	Lot 23 DP 455616	DP 455616, DP 462513	Fee Simple Title	Primary	None	North Auckland	586776	103.0	103.0	POINT (1763989.858 5916157.717)	POINT (174.84025 -36.88632)	MULTIPOLYGON (((174.84016 -36.88629, 174.84020...	Stonefields West	144900	0465305	4000293	Stonefields	517202

a. Minimum Haversine distance to coastline Hdist_coast

There are a few different datasets that could be used for this:

- NZ Coastlines (Topo 1:50k) https://data.linz.govt.nz/layer/50258-nz-coastlines-topo-150k/
- NZ Coastline - mean high water https://data.linz.govt.nz/layer/105085-nz-coastline-mean-high-water/
- NZ Coastlines and Islands Polygons (Topo 1:50k) https://data.linz.govt.nz/layer/51153-nz-coastlines-and-islands-polygons-topo-150k/  

The first doesn't have islands (e.g. Waiheke).
The second is probably most appropriate.

%%time
coastline = gpd.read_file('input/lds-nz-coastline-mean-high-water-FGDB.zip!nz-coastline-mean-high-water.gdb').to_crs(2193)
coastline = coastline.cx[bounds2193['x1']:bounds2193['x2'], bounds2193['y1']:bounds2193['y2']]
coastline_dissolved = coastline.dissolve()

%%time
parcels_sample['Hdist_coast'] = parcels_sample.apply(lambda x: x.geometry.distance(coastline_dissolved.geometry[0]), axis=1)

CPU times: user 14.3 s, sys: 0 ns, total: 14.3 s
Wall time: 14.3 s

# if distance work, then red circles should extend to the nearest coastline, and no further
subsample = parcels_sample.sample(10)
subsample['coast_buffer'] = subsample.apply(lambda x: x.geometry.buffer(x.Hdist_coast), axis=1)
subsample['geometry'] = subsample['coast_buffer']
ax = subsample.plot(color='red', alpha=0.4)
coastline.cx[1.7e6:1.8e6, 5.85e6:5.97e6].plot(ax=ax)

<AxesSubplot:>

b. Minimum Haversinedistance to Open Space(set of AUP Zones) Hdist_open

c. Minimum Haversine distance to motorway Hdist_motorway

d. Minimum Haversine distance to main arterial road Hdist_main_road

roads = gpd.read_file('input/lds-nz-road-centrelines-topo-150k-FGDB.zip!nz-road-centrelines-topo-150k.gdb').to_crs(2193)
roads = roads.cx[bounds2193['x1']:bounds2193['x2'], bounds2193['y1']:bounds2193['y2']]
highways = roads[~roads.hway_num.isna()]
highways_dissolved = highways.dissolve()

ax = highways.plot()
ctx.add_basemap(ax, crs=2193)

parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample['Hdist_motorway'] = parcels_sample.apply(lambda x: x.geometry.distance(highways_dissolved.geometry[0]), axis=1)

# if distance work, then red circles should extend to the nearest coastline, and no further
subsample = parcels_sample.sample(10)
subsample['highway_buffer'] = subsample.apply(lambda x: x.geometry.buffer(x.Hdist_motorway), axis=1)
subsample['geometry'] = subsample['highway_buffer']
ax = subsample.plot(color='red', alpha=0.4)
highways.plot(ax=ax)

<AxesSubplot:>

e. Minimum Haversine distance to rail line Hdist_rail

railroads = gpd.read_file('input/lds-nz-railway-centrelines-topo-150k-SHP.zip').to_crs(2193)
railroads = railroads.cx[bounds2193['x1']:bounds2193['x2'], bounds2193['y1']:bounds2193['y2']]
railroads_dissolved = railroads.dissolve()

parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample['Hdist_rail'] = parcels_sample.apply(lambda x: x.geometry.distance(railroads_dissolved.geometry[0]), axis=1)

# if distance work, then red circles should extend to the nearest coastline, and no further
subsample = parcels_sample.sample(10)
subsample['rail_buffer'] = subsample.apply(lambda x: x.geometry.buffer(x.Hdist_rail), axis=1)
subsample['geometry'] = subsample['rail_buffer']
ax = subsample.plot(color='red', alpha=0.4)
railroads_dissolved.plot(ax=ax)
ctx.add_basemap(ax, crs=2193)

f. Haversine distance to downtown (use Skytower coordinates) Hdist_skytower

skytower = [-36.84838748948485, 174.7621736911587]
skytower = gpd.points_from_xy(x=[skytower[1]], y=[skytower[0]])
skytower = gpd.GeoDataFrame([{"name": "Skytower", "value": 1}], geometry=skytower, crs="EPSG:4326").to_crs(epsg=2193)

parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample['Hdist_skytower'] = parcels_sample.apply(lambda x: x.geometry.distance(skytower.geometry[0]), axis=1)

# if distance works, then red circles should extend to the nearest sky tower, and no further
subsample = parcels_sample.sample(10)
subsample['skytower_buffer'] = subsample.apply(lambda x: x.geometry.buffer(x.Hdist_skytower), axis=1)
subsample['geometry'] = subsample['skytower_buffer']
ax = subsample.plot(color='red', alpha=0.2)
skytower.plot(ax=ax, color='black')
coastline.cx[1.7e6:1.8e6, 5.85e6:5.97e6].plot(ax=ax)

<AxesSubplot:>

9. Special Housing Area (SpHAs)

Indicator (1 or 0) for consent located in SpHAs SpHA_indicator
Note: here I've used centroids. Maybe should use parcel polygons instead.

spha = gpd.read_file('input/AC_Special_Housing_Area.zip').to_crs(2193)
spha_dissolved = spha.dissolve()
assert(len(spha_dissolved) == 1)

%%time
parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample['SpHA_indicator'] = parcels_sample.apply(lambda x: spha_dissolved.geometry.contains(x.geometry), axis=1)

# if distance works, then red circles should extend to the nearest sky tower, and no further
subsample = parcels_sample.sample(500)
ax=subsample.plot(column='SpHA_indicator', alpha=0.6)
plt.ylim((5.89e6, 5.95e6))
plt.xlim((1.73e6, 1.78e6))
spha_dissolved.boundary.plot(ax=ax)
ctx.add_basemap(ax, crs=spha_dissolved.crs)

10. Ranged Address Indicator

11. Matching Type Indicators

a. LINZ_MATCH_CODE

b. LINZ_2ND_MATCH_CODE